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Identification of Ixodidae ticks from cattle imported into the South Kivu province, east of the Democratic Republic of Congo

2020· article· en· W3093994939 on OpenAlexaff
M. Alphonse Bisusa, Marara Emmanuel Bizire, Bujingo Donatien Muntuokuwindi, Gilbert Nijimbere, Kashamuka Roger Musimwa, Vyambwera Guido-Charles Kambale

Bibliographic record

VenueRUDN Journal of Agronomy and Animal Industries · 2020
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsIxodidaeAmblyomma variegatumRhipicephalusGeographyBiologyVeterinary medicineInfestationIdentification (biology)AmblyommaTickDemocracyEcologyPolitical scienceLawMedicine

Abstract

fetched live from OpenAlex

A survey has been conducted in Bukavu on bovines imported in Democratic Republic of Congo from Rwanda to the public slaughterhouse of Bukavu, with the aim of identifying the Ixodidae ticks on their body. Thus, 1024 ticks have been collected on 300 cows for the entomological identification. Four species have been identified whose Boophilus decoloratus (44.4 %), Rhipicephalus appendiculatus (43.9 %), Amblyomma variegatum (11 %) and Ixodes thomasai (2 %). This last species being a new among those recognized in South Kivu. A charge to ticks of 6.5 has been observed at those bovines and the infestation rate has been significantly different in the 4 races, the Friesland (41.1 %) and Ankolé presented the superior values. The importation of cows in this part of the country from Rwanda is a real factor which conducts to the introduction of ticks and also diseases in Democratic Republic of Congo. Then being measures of heath control in the border must be sustained and reinforced for minimizing real risks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.241
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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